Grants and Contributions

In-house capability to produce two integrated microfluidic discs (Clinical Chemistry, Immunoassay) that meet required specifications and reproducibility.

The outcome will validate SyncMed’s capacity to support real-time clinical handovers with AI-driven documentation tools.

Enhancement of the ongoing work at the IP Innovation Clinic

This project will develop an innovative AI-powered data extraction vision agent to address the critical lack of integration between different software vendors in pharmacy workflows and integrate this into Medi-scribe's clinical workflow. The module will use an AI vision agent to intelligently capture patient information directly from third-party applications, eliminating manual data re-entry, reducing transcription errors, and enabling richer, more accurate documentation and data extraction.

This project aims to advance the Firm's metabolomics profiling system into a scalable, high-throughput platform for clinical and precision health applications. Building on foundational work completed in the first year, the focus now shifts to upgrading lab methods and bioinformatics tools to support the processing of over 300 samples per day, validating the system with large-scale cohorts (~30,000 samples), and developing a prototype precision health platform that integrates analytics, a database, and AI-driven health assessment tools.

With GAC’s support, the Union of Medical Care and Relief Organizations – International (UOSSM) will aim to increase access to primary healthcare services through mobile clinics and strengthened referral networks, increase screening and treatment of moderate and severe acute malnutrition among children under five, and increase access to mental health and psychosocial support (MHPSS), including for survivors of Gender Based Violence (GBV).

Through agile collaboration with veterinarians, the team gathers clinical requirements, coordinates structured interviews, and manages data acquisition and labelling.

Development of evidence-based health algorithms.

This project will provide access to IWK’s highly specialized and unique clinical resources to benefit the needs of Canadian innovation SME companies.

This project will develop a cloud-based proof-of-concept for a Clinical Decision Intelligence system powered by Large Language Models (LLMs), initially focused on cardiovascular applications with potential expansion to other medical specialties. The system will extract clinical information from various document formats, map entities to standardized medical terminologies, and leverage a knowledge graph for contextualized decision support. The prototype will demonstrate interoperability with health data standards while achieving specified performance benchmarks for accuracy, latency, and clinical relevance in cardiology workflows. Success will establish technical feasibility for future commercialization of clinical decision intelligence tools